Data Scientist – Risk

Experience: 4–5 Years

Industry: FinTech / Digital Lending / NBFC / Banking

About the Role

We are looking for a Data Scientist – Risk with 4–5 years of experience in the FinTech/Lending industry. The ideal candidate should have strong expertise in data analytics, machine learning, Python, SQL/PostgreSQL, and a solid understanding of credit and lending risk. The candidate will work closely with Risk, Credit, Business, Product, and Collections teams to build data-driven solutions and improve lending decisions.

Key Responsibilities

* Analyse customer, loan, repayment, transaction, and bureau data to identify credit-risk trends and opportunities.

- Develop and monitor credit-risk and predictive ML models for default, risk segmentation, collections, fraud, etc.
- Perform portfolio, vintage, cohort, delinquency, roll-rate, and default analysis.
- Build features, train, validate, and monitor ML models using appropriate metrics such as AUC, Gini, KS, Precision/Recall, Lift, and PSI.
- Write complex SQL/PostgreSQL queries for data extraction, analysis, and reporting.
- Use Python (Pandas, NumPy, Scikit-learn, XGBoost/LightGBM, etc.) for analytics and modelling.
- Create and maintain risk/portfolio dashboards using Metabase or other BI tools.




- Translate analytical findings into actionable recommendations for credit policy, underwriting, risk strategy, and collections.

Core Skills

* 4–5 years of experience in FinTech/Lending/NBFC/Banking/Credit Risk.

- Strong understanding of credit risk and lending lifecycle.
- Strong hands-on experience with Python, SQL/PostgreSQL, Data Analytics, and Machine Learning.
- Experience with ML models.
- Strong knowledge of feature engineering, model evaluation, and statistical analysis.
- Understanding of key lending metrics: DPD, PAR, FPD, NPA/Default, Roll Rates, Vintage Analysis, and Recovery/Loss rates.
- Solid analytical, problem-solving, and stakeholder communication skills.
- Exposure to credit bureau data, fraud analytics, or collections analytics.
- Knowledge of model monitoring, explainability, and model/data drift.

Ideal Candidate

A candidate who can independently take a problem from: Risk/Business Problem → Data Analysis → Feature Engineering → ML Model → Validation → Business Recommendation and has a strong combination of *Lending/Risk domain knowledge + Data Science + SQL + Python

Pay: ₹500,000.00 - ₹1,000,000.00 per year

Work Location: In person

📌 Data Scientist (Delhi)
🏢 Bridge - A Padmalaya
📍 Delhi